[Returnanalytics-commits] r3669 - in pkg/Dowd: . R man

[noreply at r-forge.r-project.org]

Author: dacharya
Date: 2015-06-10 20:40:35 +0200 (Wed, 10 Jun 2015)
New Revision: 3669

Added:
   pkg/Dowd/R/TQQPlot.R
   pkg/Dowd/man/TQQPlot.Rd
Modified:
   pkg/Dowd/NAMESPACE
Log:
TQQPlot: Source and Documentation

Modified: pkg/Dowd/NAMESPACE
===================================================================
--- pkg/Dowd/NAMESPACE	2015-06-10 13:06:29 UTC (rev 3668)
+++ pkg/Dowd/NAMESPACE	2015-06-10 18:40:35 UTC (rev 3669)
@@ -32,3 +32,4 @@
 export(PickandsEstimator)
 export(PickandsPlot)
 export(ProductCopulaVaR)
+export(TQQPlot)

Added: pkg/Dowd/R/TQQPlot.R
===================================================================
--- pkg/Dowd/R/TQQPlot.R	                        (rev 0)
+++ pkg/Dowd/R/TQQPlot.R	2015-06-10 18:40:35 UTC (rev 3669)
@@ -0,0 +1,134 @@
+#' Student's T Quantile - Quantile Plot
+#'
+#' Creates emperical QQ-plot of the quantiles of the data set x versus of a t 
+#' distribution. The QQ-plot can be used to determine whether the sample in x is
+#' drawn from a t distribution with specified number of degrees of freedom.
+#'
+#' @param Ra Sample data set
+#' @param df Number of degrees of freedom of the t distribution
+#' 
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#' 
+#' 
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # t-QQ Plot for randomly generated standard normal data
+#'    Ra <- rnorm(100)
+#'    TQQPlot(Ra, 20)
+#'
+#' @export
+TQQPlot<- function(Ra, df){
+  
+  x <- as.vector(Ra)
+  
+  if(!is.vector(Ra)){
+    stop("The first argument should be a vector.")
+  }
+  
+  mu <- mean(x)
+  sigma <- sd(x)
+  y<- sort(x)
+  a <- PlotPos(y)
+  x <- a$pp
+  n <- a$n
+  x <- mu + sigma * qt(x, df) # This is theoretical t-quantile
+  xx <- x
+  yy <- y
+  
+  # Dowd's code has following details but since his code does not work for 
+  # matrices with rows, columns > 1, it is not efficient to have it here.
+  # if(((dim(x)[1] == n) | (dim(x)[1] == 1 & dim(x)[2] == n)) & ~any(is.nan(x))){
+  #  xx <- sort(x)
+  # } else {
+  #   xx <- quantile(x, pvec)[[1]]
+  # }
+  #
+  # if(((dim(y)[1] == n) | (dim(y)[1] == 1 & dim(y)[2] == n)) & ~any(is.nan(y))){
+  #   yy <- sort(y)
+  # } else {
+  #   yy <- quantile(y, pvec)[[1]]
+  # }
+  xx <- sort(x)
+  yy <- sort(y)
+  
+  q1x <- quantile(x, .25)[[1]]
+  q3x <- quantile(x, .75)[[1]]
+  q1y <- quantile(y, .25)[[1]]
+  q3y <- quantile(y, .75)[[1]]
+  qx <- matrix(c(q1x,q3x), 2, length(q1x))
+  qy <- matrix(c(q1y,q3y), 2, length(q1y))
+  
+  dx <- q3x - q1x
+  dy <- q3y - q1y
+  slope <- dy/dx
+  centerx <- (q1x + q3x)/2
+  centery <- (q1y + q3y)/2
+  maxx <- max(x)
+  minx <- min(x)
+  maxy <- centery + slope * (maxx - centerx)
+  miny <- centery - slope * (centerx - minx)
+  
+  mx <- matrix(c(minx,maxx), 2, length(minx))
+  my <- matrix(c(miny,maxy), 2, length(miny))
+  
+  xmin <- min(xx, qx, mx)
+  xmax <- max(xx, qx, mx)
+  ymin <- min(yy, qy, my)
+  ymax <- max(yy, qy, my)
+
+  plot(xx, yy, type = "p", pch=3, col="red", xlab = "t-Quantiles", 
+       ylab = "Quantiles of Input Sample", 
+       main = paste("QQ Plot of Sample Data versus Student-t with ", df,
+                    "Degrees of freedom"),
+       xlim = c(xmin, xmax), ylim = c(ymin, ymax))
+  par(new = TRUE)
+  plot(qx, qy, type = "l", col="blue", xlab = "t-Quantiles", 
+       ylab = "Quantiles of Input Sample", 
+       main = paste("QQ Plot of Sample Data versus Student-t with ", df,
+                    "Degrees of freedom"),
+       xlim = c(xmin, xmax), ylim = c(ymin, ymax))
+  par(new = TRUE)
+  plot(mx, my, type = "l", xlab = "t-Quantiles", 
+       ylab = "Quantiles of Input Sample", 
+       main = paste("QQ Plot of Sample Data versus Student-t with ", df,
+                    "Degrees of freedom"),
+       xlim = c(xmin, xmax), ylim = c(ymin, ymax))
+} 
+
+# Helper Functions
+
+# Position PLot
+PlotPos <- function(Ra){
+  # 
+  sx <- as.matrix(Ra)
+  if (!is.matrix(sx)) {
+    stop("Input should be a matrix.")
+  }
+  n <- dim(sx)[1]
+  m <- dim(sx)[2]
+  if (n == 1){
+    sx <- t(sx)
+    n = m
+    m = 1
+  }
+  nvec <- sum(!is.nan(sx))
+  pp <- RepMat(as.matrix(1:n), 1, m)
+  pp <- (pp - .5)/ RepMat(nvec, n, 1)
+  pp[is.nan(sx)] <- NaN
+  
+  if (nargs() > 1){
+    n <- max(nvec)
+  }
+  return(list("pp" = pp, "n" = n))
+  
+}
+
+# Implementation of repmat from matlab in R
+RepMat <- function(X,m,n){
+  X <- as.matrix(X)
+  mx <- dim(X)[1]
+  nx <- dim(X)[2]
+  a <- matrix(t(matrix(X, mx, nx * n)), mx * m, nx * n, byrow = T)
+  return(a)
+}
\ No newline at end of file

Added: pkg/Dowd/man/TQQPlot.Rd
===================================================================
--- pkg/Dowd/man/TQQPlot.Rd	                        (rev 0)
+++ pkg/Dowd/man/TQQPlot.Rd	2015-06-10 18:40:35 UTC (rev 3669)
@@ -0,0 +1,30 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/TQQPlot.R
+\name{TQQPlot}
+\alias{TQQPlot}
+\title{Student's T Quantile - Quantile Plot}
+\usage{
+TQQPlot(Ra, df)
+}
+\arguments{
+\item{Ra}{Sample data set}
+
+\item{df}{Number of degrees of freedom of the t distribution}
+}
+\description{
+Creates emperical QQ-plot of the quantiles of the data set x versus of a t
+distribution. The QQ-plot can be used to determine whether the sample in x is
+drawn from a t distribution with specified number of degrees of freedom.
+}
+\examples{
+# t-QQ Plot for randomly generated standard normal data
+   Ra <- rnorm(100)
+   TQQPlot(Ra, 20)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+